I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t
Every AI app I've shipped recently rewrote the same plumbing. The OAuth dance for Slack. Encrypted storage for an API key. Refresh-token logic that finally fails on the 3rd call after an hour. Wiring up an MCP client to a server behind a bearer token someone pasted into a Notion page.